Family Connections in Motorsports: The Case of Formula One*

Craig A. Depken, II

Department of Economics

University of North Carolina – Charlotte

Peter A. Groothuis

Department of Economics

Appalachian State University

Boone, NC 28608

Kurt W. Rotthoff

Department of Economics and Legal Studies

Seton Hall University

South Orange, NJ 07079

*We would like to thank Trey Edgerton for research assistance and participants at the Eastern

Economic Association Meetings, Western Economics Association Meetings, and Southern

Economic Association Meetings.

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Family Connections in Motorsports: The Case of Formula One

Abstract

Within-family career following is common in many occupations including law, politics,

business, agriculture, medicine, entertainment, and professional sports. For children who enter

the same career as their parents there are several potential benefits: physical-capital transfer,

human-capital transfer, brand-name-loyalty transfer, or nepotism. In Formula One (F1) auto

racing, career following is also common; many drivers follow their father or brother into racing

at that level. Using a panel describing F1 drivers from 1953-2011, we find that brothers of F1

drivers appear to benefit from human capital transfer and nepotism and that sons gain little from

human capital transfer and do not enjoy nepotism. We do find that only the best drivers have

sons who follow them into racing, suggesting that sons can extend the brand name-loyalty

perhaps long after their fathers have retired.

Key words: Motorsports, Nepotism, Human Capital, Brand Loyalty.

JEL Classifications: L83, Z20

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1. Introduction

The relationship between a parent’s career and a child’s career choice has been the interest of

researchers across several fields. In economics, Laband and Lentz have studied career following

by children in a variety of industries. Not surprising, the reasons for following a parent into the

same career vary by industry. For example, Laband and Lentz (1983b) find that children of

farmers who also become farmers tend to farm the same land as their parents, suggesting both

human capital transfer, in the form of knowledge of how to farm, and physical capital transfer, in

the form of the land and equipment required to farm. In the United States, nearly fifty percent of

self-employed proprietors are second-generation business owners, suggesting that name brand

loyalty, human-capital transfer, and physical-capital transfer might all influence the child’s

choice. Laband and Lentz (1990a) find that the sons of baseball players tend to play the same

position as their fathers, suggesting human capital transfer either in the form of natural ability or

knowledge of how to train and play at the highest level.

Laband and Lentz (1985) find that the children of politicians are more likely than the

children of non-politicians to become politicians. Furthermore, the children of politicians do

better than their parents in winning elections. The evidence suggests that politics is characterized

by brand name loyalty and human-capital transfer where parent politicians teach their children

how to also be successful politicians.

Laband and Lentz (1992) find that the children of lawyers tend to do better in the early

years of their own law practice than the children of non-lawyers. The evidence suggests that the

practice of law can be characterized by human capital transfer, if parents teach their children how

to be a successful lawyer, physical-capital transfer, if parents hand a successful practice to a

child, and nepotism, if the children of lawyers are accepted to better law schools or provided

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with higher valued opportunities after law school simply because they are the children of

lawyers. Nepotism appears to be an issue in medical school admissions in the United States;

Laband and Lentz (1990b) find that the children of doctors have an advantage in medical school

admission even if they have lower test scores or grades.

The question asked in this paper is whether there are benefits to family connections in F1

racing. The question appears pertinent because family connections appear to be important in

other areas of auto racing. For instance, in 2005 23 out of 76 NASCAR drivers had a family

connection. Groothuis and Groothuis (2008) find no nepotism in NASCAR when it comes to

career length but that the father of a current driver is more likely to exit the circuit in a given

year. They suggest that fathers of drivers may leave early because the son is able to extend any

brand name loyalty. In addition, Rotthoff, Depken, and Groothuis (2014) find that in NASCAR

sons of former racers are more likely to be on camera then their performance would indicate,

which suggests brand loyalty transfer. In F1 racing, twelve sons have followed their fathers in

that circuit and there have been many brothers who raced at the same time. To date instances of

career following in F1 has been male, therefore we use the designation of father, son, and

brother. 1

Using a panel of annual statistics for F1 drivers from 1950-2011, we investigate whether

sons and brothers start their careers earlier and are better early in their career (human capital

transfer), whether fathers are better drivers with longer careers than non-father drivers (brand

name loyalty), and whether sons and brothers have longer careers than their productivity would

suggest (nepotism). To preview our results, it appears that F1 is characterized by a weak form of

1 Historically, male participants have dominated motorsports. However, there are female drivers in NASCAR,

NHRA, Formula 3, ARCA, and rally circuits. Ashley Force Hood and Courtney Force, daughters of legendary drag

racer John Force, both compete in NHRA events.

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human capital transfer, with the potential for brand name loyalty transfer between fathers to sons,

and that brothers (but not sons) may experience nepotism.

2. Family connections in Formula One Racing: Testable Hypotheses

2.1 Human-Capital Transfer

Formal education is one common way to acquire general human capital. In the United

States, a high-school education is expected to provide sufficient knowledge and skills to be

successful in college or the work force (Kendall, et al., 2007). However, firm specific human

capital is often acquired through on-the-job training in what might be considered a shared

investment between the firm and the employee (Becker, 1993). Furthermore, many occupational

skills are learned informally on the job, such as learning by doing in farming, being a sole

proprietor, or learning a corporate culture.

In sport, many of the skills required for success fall between formal and informal

education; strategy and tactics might be something learned through study and practice but innate

ability might be augmented with physical training and nutrition. Still other sports skills can only

be obtained by participating in the sport through learning by doing. In North America, baseball,

hockey, basketball, and soccer use minor league teams to develop player talent, whereas

American football develops skills in college athletics. In F1 racing, several lower series, such as

Formula 3, GP2, and Formula 3000 (formerly Formula 2), provide avenues for drivers to develop

their skills.

Children from racing families have an advantage over children in non-racing families in

that they grow up in the tradition of racing, can acquire skills and knowledge by being at the

track and in the garage with their families, and by having family members who might have plans

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for intergenerational transfer of brand name loyalty or racing-specific capital recourses. For

example, although Nico Rosburg was born after his dad, Keke Rosburg, won the 1982 world

championship, as Nico progressed through the developmental circuit, he had his F1 World

Champion father in his pits. Laband and Lentz (1983a) suggest that occupation-specific human

capital can be acquired as a by-product of growing up around elders with the same occupation-

specific human capital, even proposing that some human capital is essentially free for career

followers.2 If this type of human-capital spillover is present in F1 racing, we expect to see sons

and brothers entering the circuit at a younger age than drivers not related to previous F1 drivers.

Furthermore, if human capital transfer is important in F1 racing, drivers with family connections

should experience more success early in their careers then drivers without family connections.

This leads to two testable hypotheses:

H1: Sons and brothers of F1 drivers are no younger than other drivers at their debut;

H2: Sons and brothers of F1 drivers have no more success early in their careers than other

drivers.

2.2 Brand Name Loyalty

In F1 racing, the details about sponsorship contracts are tightly held and are generally not

publicly available. It is speculated that sponsorship revenue often comprises more than 50% of a

team’s income with the remainder coming from race prize money and shares in media revenues

(Tierney and Fairlamb, 2002). Thus, team owners seek increasing sponsor dollars to provide

more financial capital to finance team operations. Corporations sponsor teams to advertise their

products and gain exposure for their corporate names. Drivers in many ways become a

2 For a formal model of human capital transfer between generations see Laband and Lentz (1983a). In their model

the develop conditions when children acquire their education at home and when they acquire their education

formally at school. Our hypothesis is that in Formula One Racing many skills can be transferred informally from

fathers to sons.

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spokesperson for the corporations that sponsor his team. Thus, the driver’s last name often

becomes associated with a corporation and can become a brand of its own; for instance, three-

time F1 World Campion, Lewis Hamilton, is known for his connection with the Mercedes AMG

Petronas team (and likewise the team’s sponsors).3

Laband and Lentz (1985) contend that occupational following may be an efficient

mechanism for the transfer of rents across generations when the family name embodies goodwill.

They argue this occurs in politics when several family members seem to run more on the family

name rather than their inherent abilities as a politician. Examples in the United States might

include family names such as Kennedy, Clinton, or Bush and in the United Kingdom might

include family names such as Kinnock or Benn.

If a family name provides a marketing advantage in F1, then team owners may hire

family-connected drivers of lower ability because of fan, consumer, or sponsor preferences. In

some ways, brand name loyalty follows Becker’s (1975) model of customer-based discrimination

where team owners hire less productive drivers to please sponsors. It appeals to sponsors because

fan loyalty to a family name leads to more sales even if the driver is not as productive as other

drivers. If family name loyalty is present in F1, we should find that only the most productive

drivers have sons follow them into racing as these fathers have developed the greatest potential

rents from their family name. This leads to our third testable hypothesis:

H3: F1 drivers with sons who become drivers are no more productive than drivers without sons

who become drivers.

2.3 Nepotism

Intuitively, nepotism is a form of Becker’s employer-based discrimination (Becker 1962).

In Becker’s original model, firm owners gain disutility in hiring members of a particular group.

3 As of this writing, Lewis Hamilton had won the F1 circuit in 2008, 2014, and 2015.

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Nepotism, on the other hand, is the result of a firm owner gaining positive utility from hiring

family-connected workers. Fathers might gain positive utility from hiring their child even if more

productive workers are available; hence, the popularity of the “and sons” (and increasingly of

“and daughters”) in firm names. In motorsports, nepotism would imply sons of F1 drivers having

longer careers than their productivity would otherwise suggest. This leads to our fourth testable

hypothesis:

H4: Sons and brothers of F1 drivers have careers no longer than non-family connected drivers.

To review, there are many, not mutually exclusive, reasons for children to follow a parent

into a career in motorsports. Human-capital transfer contends that family-connected drivers enter

racing at a younger age and might be more productive in the early years of their career. Brand

name loyalty states suggests that only the best drivers have sons follow them into racing. Finally,

nepotism argues that family-connected drivers have longer careers than their productivity would

suggest relative to drivers without family connections. The next section describes the data we use

to test these various hypotheses in F1 racing.

3. The Data

To test our hypotheses, we use a panel describing all drivers in the F1 series from 1950

through 2011. This sixty-year panel consists of 728 drivers and 2693 observations. Using various

data sources, we identified drivers who are father-son relatives and drivers who are brother-

brother relatives. Some drivers are brothers without being the sons of another driver and some

drivers are the father of another professional driver who did not compete in the F1 circuit. Table

1 reports those drivers identified as fathers, sons, and brothers in the F1 circuit.

[Table 1]

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Table 2 provides cross-tabulations of the brothers and sons, fathers and sons, and fathers

and brothers. As can be seen, there are ten drivers who are both a sons and a brother, for

example, Michael and Mario Andretti, and fifty-three drivers who are a brother but not a son of

an F1 driver. Five drivers are both the father and a son of another professional driver and sixteen

fathers are also a brother of another professional driver.

[Table 2]

Table 3 reports the descriptive statistics of the entire sample and for each category of

family connection. The data include age as well as performance data such as wins, podiums, laps

led, races, and average finish. The average number of races per driver-year is approximately

seven, per-season wins average 0.31, podium finishes average .94, and laps led per-season

averages 20.74.4 The average age in F1 is 31 with the youngest driver being 19 and the oldest 56.

[Table 3]

In Table 3, we report the means by family connection, comparing those with family

connections to those with no family connections. We find that all performance variables are

better in the sub-categories of family connections compared to drivers without family

connections. On average, fathers tend to do better than sons, while brothers do better than sons

but worse than fathers. The average career length, as measured by all non-right censored

observations, ranges from 3.70 years for drivers without family connections to 6.24 years for

fathers. The careers of sons average 4.35 years and those of brothers average 6.20 years. Sons

and brothers start their careers at the average age of 27, fathers start at the average age of 28, and

drivers without family connections start their career at the average age of 31.

On the surface, the averages are consistent with nepotism, brand transfer, or human-

capital transfer and all might cause career following in the F1 circuit. To further explore the

4 A podium finish occurs when the driver finishes in the top three.

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importance of family relations and determine if nepotism exists in F1, we analyze the data using

parametric, non-parametric, and semi-parametric techniques.

4. Human Capital Transfer and Brand Loyalty

Sons and brothers of drivers might have inherent advantages because they grow up in and

around a racing environment. The human capital transfer from fathers to sons and from brother

to brother might cause sons and brothers to be better drivers at a younger age, thereby increasing

the odds that these individuals would be hired to drive for an F1 team at a younger age than non-

family-tied drivers. To test this hypothesis, we test whether there is a statistically significant

difference in starting age between sons and non-sons and brothers and non-brothers. The results

of these tests are reported in Table 4a and show that both sons and brothers start their career in

F1 at younger ages than non-sons and non-brothers. Sons start driving, on average, when they are

28.6 years of age whereas non-sons average 31.47 years of age when they start driving. Brothers

start driving, on average, when they are 30.33 years of age whereas non-brothers average 31.53

years of age when they start driving. Both differences are statistically significant at the five

percent level and suggest human capital transfer within F1 racing.

A second hypothesis about human capital transfer is that sons and brothers perform better

early in their careers. To test this, we compare four common productivity measures between sons

and non-sons and brothers and non-brothers after three years of racing in the F1 circuit: total

races completed, total wins, total podiums, and total laps led. The results are reported in Table

4a. While both sons and brothers complete more races than non-sons and non-brothers,

respectively, in their first three years, sons do not have more wins, podiums, or laps led than non-

sons after three years. However, brothers do have more wins, podiums, and laps led than non-

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brothers after three years of racing. In the case of sons, we find no evidence that the four

performance measures are jointly statistically different from non-son drivers. However, for

brothers we do find evidence that their production statistics are jointly statistically different from

non-brother drivers. Therefore, while both sons and brothers exhibit human capital transfer by

starting their careers earlier and having a few more races completed after three years, it appears

that brothers enjoy more productivity benefits from human capital transfer than sons.

[Table 4]

A third hypothesis about family connections in F1 is that fathers who have sons in racing

are themselves among the best drivers. This allows the driver fathers to capitalize on their brand

(family) name through future generations of drivers, even if their son drives long after they

retire. If a lower-quality driver has no brand loyalty, this would reduce the incentive to hire or

encourage the next generation to enter the circuit. We aggregate each driver’s career across all

years and test whether fathers are statistically better than drivers without family connections in

seven categories: age at end of career, total races, total laps, average finishing position, total

wins, total podiums, and total laps led. The results for these tests are reported in Table 4b.

Fathers of drivers end their careers at an average age of 35.9 whereas non-fathers (who

are also non-sons and non-brothers) end their career at an average age of 32.9 (the difference is

statistically significant at the five percent level). Over the course of their careers, fathers

complete more forty more races than their peers, complete 1967 more laps on average, and

finishing 1.69 positions better on average. While having careers 3.67 years longer on average

can contribute to more races and laps completed, fathers are also better drivers as reflected in

having 4.5 more wins on average, 10.2 more podiums on average, and 289 more laps led on

average during their career. We find that for fathers these productivity differences are jointly

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statistically different from zero. This is consistent with brand name recognition having value in

F1 as it does in other areas.

Table 4b also reports the conditions for brand name loyalty for sons and brothers at the

end of their careers. The evidence suggests that sons do not have jointly significantly different

productivity statistics at the end of their careers compared to non-son drivers. On the other hand,

brothers do have jointly significantly different and better production statistics at the end of their

career compared to non-brother drivers. This suggests that not only do brothers receive more

human capital transfer compared to sons but brothers also end their careers with greater potential

brand name loyalty, which they could pass along to the next generation of drivers.

5. Nepotism in Formula One: Evidence from Career Duration

The possibility of nepotism in F1 racing is the final hypothesis we test. We define

nepotism as sons or brothers of F1 drivers having longer careers than non-son and non-brother

driver, holding quality constant. Estimating career lengths using standard OLS techniques has

well-known problems. Therefore, we analyze the career lengths of F1 drivers via non-parametric

and semi-parametric methods.

5.1 Non-parametric Estimation

To investigate career duration in F1 racing, we calculate yearly hazard rates as:

(1) ht = dt / nt,

where dt is the number of drivers who end their career in year t and nt is the number of drivers at

risk of ending their career in year t. The hazard rate can be interpreted as the percentage of

drivers who exited F1 at the end of a given season, given their level of tenure at time t. We

suspect that most exit was involuntary, particularly for drivers with short careers, although our

data do not indicate whether exit was voluntary or not.

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In Table 5, we report the total hazard rate, the hazard rate for drivers with no family

connections, and the hazard rate for those drivers with family connections of being a father, son,

or brother. We find that family-connected drivers are less likely to exit early in their F1 career

than non-family connected drivers. Drivers who become fathers of drivers have the lowest

probability of exit at any given level of tenure. Brothers have a lower probability of exit

compared to drivers without family connections at all levels of tenure less than ten years. Sons

have a higher probability of exit than both brothers and fathers but generally a lower probability

of exit than drivers without family connections.

[Table 5]

In Figure 1, we plot the hazard rate by family-connection category. The plot of drivers

without family connections shows that the hazard rate gradually declines for the first four years

of the average driver’s career and then levels out. Yet, the hazard rate is always higher than for

family-connected drivers except in the last few years for sons. Sons also show a gradual decline

in career exit during the first four years of their career before their exit probability levels off.

Brothers and fathers exhibit relatively low and steady exit probabilities with some jumps around

seven years of experience.

Comparing the plots, it appears that drivers with family connections are somewhat less

likely to exit F1 during the first ten years of their career as their hazard rates are consistently

lower than those of drivers without family connections. After ten years, however, both the

brother and son hazard rates and the hazard rates of drivers without family connections cross

while the hazard rates of fathers always remain lower than the hazard rate of drivers without

family connections. While the non-parametric approach suggests there are differences in career

length between family-connected and non-family-connected drivers, this methodology cannot

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determine if these differences are due to productivity differences or nepotism. We therefore

move to semi-parametric techniques to control for differences in productivity.

5.2 Semi-parametric Estimation

Methodology

To capture the overall length of a driver’s career, our data contains only flow samples

because 1950 is the first year of the series. As with most panels, our data are right-censored

where many careers were ongoing when our sample ends in 2011. We estimate semi-parametric

hazard functions following Berger and Black (1999), Groothuis and Hill (2004), and Groothuis

and Groothuis (2008). Because our data are at the season level, we calculate our hazard model

as a discrete random variable. As with Groothuis and Hill (2004), we model the durations of a

single spell.

We also assume a homogeneous environment so that the length of the spell is

uncorrelated with the calendar time in which the spell begins, except for a time trend variable.

This assumption lets us treat all a driver’s tenure of a given length of time as the same regardless

of when it occurred in the sample period. For instance, all fourth-year drivers are assumed to

have the same base-line hazard, regardless of calendar time. This implies that a fourth-year

driver in 1960 has the same baseline hazard as a fourth-year driver 2010, apart from a time trend

(for the technical details of the model see Groothuis and Hill, 2004.)

The hazard rate is modeled as the conditional probability of exiting F1 series, given that

the F1 career lasted until the previous season. Because the hazard function must have a range

from zero to one, in principle any mapping with a range from zero to one can be used. Cox

(1972) recommends

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(2) h t x

h t x

h

he xt

t

xt

( , , )

( , , )exp( )

1 1

,

which is simply a logit model with intercepts that differ by time periods. The term ht is a baseline

hazard function, common to all observations; the x term, which reflects the driver’s personal

and productivity characteristics, shifts the baseline hazard function, but it affects the baseline

hazard function in the same way each period. Berger and Black (1999) consider other hazard

functions and find that the results are relatively robust across various specifications of the hazard

function. We follow Cox and use the logit model.

The intuition behind equation (2), when using the logit model for the hazard function, is

relatively simple. At the end of each year during the sample period during which a driver races in

F1, the driver either comes back for another season or ends his career. If the driver’s career ends,

the dependent variable takes on a value of one, and zero otherwise. The driver remains in the

panel until either the driver exits F1 or the panel ends. If the panel ends before the driver

explicitly exists F1, the worker’s spell is considered right-censored. Thus, a driver who begins

his F1 career during the panel and races for six years will enter the sample six times. The value

of his dependent variable will be zero for the first five years (tenure year one through year five)

and be equal to one for the sixth year.

Because the drivers in the panel have varying career lengths we can identify the hazard

function for both long and short careers. The disadvantage to this approach is that the vector t in

equation (2) can be very large; here it would require 19 dummy variables. Another complication

is that in F1 there are few drivers with very long careers, thereby making it difficult to precisely

estimate the dummy variables in t that correspond with the longest careers. To simplify the

computation of the likelihood function and keep those few observations for drivers with long

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careers, we approximate the t vector with a 5th order polynomial in driver’s tenure. This reduces

the number of parameters to be estimated from 19 to five. The hazard function becomes

(3) h t x

h t xt e t xx( , , )

( , , )( ) exp( ( ) )

1 ,

where (t) is a 5th

order polynomial in the driver’s tenure. This method provides a very flexible

specification of the baseline hazard, but does impose more restrictions than Cox’s model.5

Estimation Results

In Table 6 we report the estimates for two specifications of equation 2. In Model (1),

reported in Column 1, we include only the dummy variables for family connections and

continuous or nearly continuous positive performance measures; column 2 reports the marginal

effects evaluated at the sample means (or discrete changes for indicator variables). In Model (2),

reported in Column 3, we include the family dummy variables and negative performance

measures; Column 4 reports the marginal effects evaluated at the sample means (or discrete

changes for indicator variables).

[Table 6]

In the first specification, we find that performance measures influence the likelihood of

racing the next season. The more podiums, races completed, and laps completed in a season the

less likely is a driver of leaving F1 racing. Furthermore, the better the average finish of the driver

during the season the less likely they are to leave F1 racing that year. It appears that number of

races won and laps led over the season are not significant influences on drivers leaving F1. The

5 When higher order polynomials (the sixth and seventh power) are included, the results do not change. This

suggests that a fifth order polynomial is flexible enough to capture the influence of the base line hazard.

17

age of the driver is positively correlated with leaving F1 racing. The time trend is positively

correlated with exit suggesting that recent drivers are more likely to exit F1 racing each year, all

else equal, than drivers in the past. This finding might suggest a greater level of competition

among potential F1 drivers in recent years than in the past.

The coefficients on family connections provide interesting results. For ease of

interpretation we convert the coefficients by 100[exp() -1], which yields the percentage

difference in hazard rates between the different family connections. From Model (2), fathers are

33.5 percent less likely to exit, other factors constant; being a son does not impact career exit in a

statistically significant fashion; and being a brother lowers the likelihood of exit by

approximately 19 percent. The results suggest some nepotism in F1 directed toward brothers

(rather than sons); brothers have longer careers than non-brothers after controlling for quality.

Model (3) replaces the positive productivity measures of wins, podiums, laps led, total

laps completed, and average finishing position, with negative productivity measures: indicator

variables for having never led a lap during the season, never winning during the season, and

never having a podium during the season. In this case, the results suggest that never leading a lap

and never having a podium both contribute to increased probability of exiting F1 (7.35 percent

and 11.74 percent, respectively). Fathers and brothers are still less likely to exit F1, all else

equal, and sons do not seem to experience any different career length.

Overall, the evidence suggests that fathers have longer careers than non-fathers (who are

also non-sons and non-brothers) perhaps because of the brand name recognition they develop

over their career. The brand name recognition that the driver has developed can then be

extended by a son who eventually enters professional racing, most often years after the father has

retired. We define nepotism as extending the career of a family member beyond what their

18

productivity would suggest. Only brothers seem to enjoy any impact of nepotism on their career

length; sons do not experience any longer careers than drivers who are not sons (or fathers or

brothers).

6. Conclusions

This paper investigates the impact of family connections in F1 racing. Family

connections have proven important in other industries, including law, acting, and sports

(including other forms of motorsports). Children might follow their parents in a career because

of human capital transfer between parents and children, brand-name recognition, or nepotism.

We test all three of these possibilities in F1 using data describing drivers in that circuit from

1950 through 2011.

We find evidence that sons and brothers of F1 drivers both enter the circuit at a lower age

but only brothers seem to be more productive early in their careers. Sons of drivers are no better

than non-son drivers in wins, podiums, or laps led during the first three years of their career;

drivers who are brothers of other drivers are better than non-brother drivers in each of these

categories. This suggests that while both sons and brothers gain some human capital transfer, it

appears brothers gain more.

We test whether fathers are better drivers than drivers who do not have a son follow them

into professional racing. We find that fathers tend to end their careers at an older age than non-

fathers, and that fathers are better than non-fathers in terms of total wins, total podiums, total

laps led, and average finishing position. This suggests that those drivers who have a son follow

them into racing are from the best drivers. This supports the idea that fathers build brand-name

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recognition, which is transferred to their children even if this occurs years after the father has

retired from racing.

Finally, we test whether career length in years is impacted by productivity measures and

family connections. We find that, holding productivity measures constant, drivers who become

fathers of future professional racers are less likely to exit F1, supporting the previous intuition

that such drivers seek to build brand-name recognition. Being the son of a driver does not

influence the odds of exiting, suggesting that there is no nepotism for sons. On the other hand,

being a brother of a driver reduces the odds of exit by approximately six percent, holding

productivity constant. Thus, there appears to be nepotism directed toward brothers – their careers

are longer than their productivity measures suggest. Therefore, it appears that family connections

are important for certain drivers in F1 as they are in other industries.

20

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Transfers Among Politicians” Economic Inquiry, 23(3), pp. 395-414.

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Economic Interpretation” Sportometrics, TexasA&M University Press.

Laband, David N. and Bernard F. Lentz (1990b), “Why so Many Children of Doctors Become

Doctors: Nepotism vs. Human Capital Transfer” Journal of Human Resources, 24(3), pp.

396-413.

21

Laband, David N. and Bernard F. Lentz (1992), “Self-Recruitment in the Legal Profession”

Journal of Labor Economics, 10(2), pp. 182-201

Laband, David N. and Bernard F. Lentz (1995), “Workplace Mentoring in the Legal Profession”

Southern Economic Journal, 61(3), pp.783-802.

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Volume: 46, Issue: 19, pages 2277 – 2289.

Tierney, Christine and David Fairlamb (2002), “Formula One: So Far, No Checkered Flag,”

Bloomberg News, June 3, 2002, available at

http://www.bloomberg.com/news/articles/2002-06-02/formula-one-so-far-no-checkered-

flag, las accessed August 2016.

22

Table 1: Family Connections in Formula One (1950-2011)

FATHERS

SONS

BROTHERS

First Last First Last First Last First Last First Last

Mario Andretti Niki Lauda Cliff Allison Michele Alboreto Tim Mayer

Michael Andretti Jan Magnussen Michael Andretti Cliff Allison Stirling Moss

Julian Bailey Nigel Mansell Alberto Ascari Mario Andretti Kazuki Nakajima

Edgar Barth Satoru Nakajima Sebastien Bourdais Michael Andretti Larry Perkins

Derek Bell Jonathan Palmer David Brabham Jean Behra Nelson Piquet Jr.

Tony Bettenhausen Olivier Panis Jenson Button Stefan Bellof Didier Pironi

David Brabham Roger Penske Colin Davis Lucien Bianchi Kimi Raikkonen

Jack Brabham Paul Pietsch Christian Fittipaldi David Brabham Dick Rathman

Martin Brundle Andre Pilette Gregor Foitek Ernesto Brambilla Jim Rathmann

Ronnie Bucknum Nelson Piquet Gene Hartley Vittorio Brambilla Peter Revson

Adrian Campos Alain Prost Alan Jones Martin Brundle Pedro Rodriguez

Duane Carter Bobby Rahal Pierluigi Martini Eddie Cheever Jr. Ricardo Rodriguez

Erik Comas Keke Rosberg Stirling Moss Patrick DePailler Troy Ruttman

Derek Daly Louis Rosier Kazuki Nakajima Jose Dolhem Ian Scheckter

Emilio de Villota Paul Russo Tim Parnell Corrado Fabi Jody Scheckter

Jean-Denis Deletraz Bob Said Andre Pilette Teo Fabi Harry Schell

Mark Donohue Ian Scheckter Teddy Pilette Luigi Fagioli Michael Schumacher

Guy Edwards Jody Scheckter Nelson Piquet Jr. Ralph Firman Ralf Schumacher

Teo Fabi Michael Schumacher Nico Rosberg Emerson Fittipaldi Jackie Stewart

Juan Manuel Fangio Jo Siffert Harry Schell Wilson Fittipaldi Jimmy Stewart

Wilson Fittipaldi Jackie Stewart Mike Taylor Marc Gene Maurice Trintignant

Elmer George John Surtees Michael Thackwell Roberto Guerrero Bobby Unser

Dan Gurney Piero Taruffi Bobby Unser Hubert Hahne Jerry Unser

Jim Hall Bobby Unser Rikky von Opel Lewis Hamilton Gijs van Lennep

Graham Hill Jerry Unser Markus Winkelhock Nick Heidfeld Gilles Villeneuve

Kazuyoshi Hoshino Jos Verstappen Alexander Wurz Damon Hill Jacques Villeneuve

James Hunt Gilles Villeneuve

James Hunt Luigi Villoresi

Jacky Ickx Bill Vukovich

Alan Jones Derek Warwick

Alan Jones Manfred Winkelhock

Jan Lammers Graham Whitehead

Jacques Laffite

Chico Landi Peter Whitehead

Nicola Larini Justin Wilson

Pierluigi Martini Manfred Winkelhock

23

Table 2: Cross Tabulations of Family connections

BROTHERS

SONS NO YES TOTAL

NO 655 53 708

YES 16 10 26

TOTAL 671 63 734

FATHERS

SONS NO YES TOTAL

NO 654 54 708

YES 21 5 26

TOTAL 675 59 734

FATHERS

BROTHERS NO YES TOTAL

NO 628 43 671

YES 47 16 63

TOTAL 675 59 734

24

Table 3: Descriptive Statistics

Total Sample No Family Father Son Brother

Exit .27

(.45)

.31

(.46)

.14

(.35)

.04

(.42)

.14

(.37)

Age 31.41

(6.10)

31.40

(6.13)

32.90

(6.04)

28.58

(4.99)

30.34

(5.75)

Tenure 3.95

(3.28)

3.51

(2.97)

5.55

(3.94)

4.17

(2.98)

5.02

(3.47)

Races 7.10

(6.30)

6.27

(6.17)

9.43

(5.81)

8.85

(6.55)

9.83

(6.15)

Wins .31

(1.11)

.16

(.75)

.95

(1.95)

.46

(1.24)

.74

(1.84)

Podiums .94

(2.24)

.64

(1.81)

2.07

(3.22)

1.02

(2.29)

1.81

(3.11)

Laps Led 20.74

(70.19)

11.87

(51.58)

59.21

(118.41)

30.18

(75.61)

45.15

(106.96)

Laps Completed 340.08

(306.47)

298.55

(293.68)

453.41

(295.88)

432.10

(339.27)

472.03

(319.98)

Average Finish 5.53

(5.39)

4.93

(5.29)

7.46

(5.50)

6.73

(5.31)

7.12

(5.03)

Never Led .78

(.41)

.83

(.37)

.61

(.49)

.69

(.46)

.65

(.48)

Never Won .88

(.32)

.92

.27)

.69

(.46)

.84

(.37)

.77

(.42)

Never Podium .74

(.73)

.79

(.40)

.54

(.50)

.71

(.46)

.56

(.50)

Sample Size 2,733 1,988 405 113 392

Notes: Standard deviations reported in parentheses.

25

Table 4a: Human Capital Transfer to Sons and Brothers

Human Capital Transfer Sons Brothers

H1: Age at Debut -2.86***

(4.92)

-1.19***

(3.62)

H2: Productivity in First Three Years

Average Finishing Position -1.41

(1.17)

-1.12**

(0.66)

Total Wins 0.31

(0.38)

0.95***

(0.21)

Total Podiums -0.41

(.1.01)

2.79***

(0.55)

Total Laps Led 8.08

(0.25)

54.58

(0.14)

Joint Test of Significance (F4,1336) 1.92 7.60*** Notes: Sample describes productivity for 336 Formula One drivers who had a

career at least three years long. Differences reported between sons/brothers

against non-sons/non-brothers. Absolute values of t-statistics reported in

parentheses. *** p<0.05, ** p<0.10.

Table 4b: Conditions for Brand Name Loyalty at End of Career

Productivity Measure Fathers vs. Non-

Father Peers

Sons vs. Non-

Son Peers

Brothers vs. Non-

Brother Peers

Average Finishing Position -1.69***

(2.17)

0.41

(0.32)

-2.18***

(2.79)

Age at Career End 2.95***

(2.83)

-4.68***

(2.77)

0.02

(0.02)

Total Races 40.44***

(5.99)

14.19

(1.34)

35.14***

(5.22)

Total Laps 1967.89***

(6.11)

852.52*

(1.68)

1732.55***

(5.38)

Total Wins 4.58***

(7.47)

1.13

(1.49)

2.10***

(4.21)

Total Podiums 10.25***

(6.59)

2.32

(1.07)

6.87***

(4.89)

Total Laps Led 289.60***

(7.21)

687.75

(1.31)

1155.33***

(3.42)

Test for Joint Significance

(F7,5124)

13.32*** 2.30*** 7.73***

Notes: Absolute value of t-statistics reported in parentheses. *** p<0.05, ** p<0.10.

26

Table 5: Career Exit Hazard Rates

Tenure

No Family

connections Father

Son Brother

1 .379 .100 .269 .143

2 .363 .122 .289 .154

3 .309 .133 .250 .111

4 .244 .105 .125 .091

5 .253 .107 .095 .162

6 .277 .135 .211 .148

7 .235 .138 .200 .102

8 .279 .125 .083 .150

9 .310 .139 .182 .222

10 .250 .162 .222 .229

11 .255 .161 .429 .333

12 .286 .115 .750 .444

Max Tenure 19 years 18 years 12 years 18 years

27

Table 6: Determinants of Career End

(1) (2) (3) (4)

VARIABLES Exit (1=Yes) dPr(Exit)/dX Exit (1=Yes) dPr(Exit)/dX

FATHER -0.408*** -0.111*** -0.401*** -0.108***

(0.084) (0.020) (0.085) (0.020)

SON 0.122 0.039 0.127 0.040

(0.123) (0.040) (0.130) (0.043)

BROTHER -0.210** -0.060** -0.193** -0.055**

(0.087) (0.023) (0.088) (0.024)

YEAR 0.025*** 0.008*** 0.024*** 0.007***

(0.003) (0.001) (0.003) (0.001)

AGE 0.046*** 0.014*** 0.045*** 0.014***

(0.005) (0.002) (0.005) (0.002)

RACES -0.085*** -0.026*** -0.084*** -0.025***

(0.017) (0.005) (0.008) (0.002)

WIN 0.191* 0.058*

(0.113) (0.034)

PODIUM -0.094** -0.029**

(0.037) (0.011)

LAPSLED -0.162 -0.049

(0.159) (0.048)

LAPS -0.007 -0.002

(0.036) (0.011)

AVE FINISH 0.013** 0.004**

(0.005) (0.002)

NEVERLED

0.246** 0.071**

(0.111) (0.030)

NEVERWIN

-0.163 -0.052

(0.150) (0.049)

NEVERPODIUM

0.392*** 0.111***

(0.104) (0.027)

CONSTANT -51.740***

-49.797***

(5.178)

(5.248)

Observed Probability 0.267 0.267

Predicted Probability 0.231 0.229 All models include 2,693 observations for F1 drivers from 1950-2011. Robust standard errors

clustered by driver reported in parentheses. Marginal effects evaluated at the sample means for continuous variables; evaluated using discrete changes for indicator variables. Predicted

probability evaluated at sample means. *** p<0.01, ** p<0.05, * p<0.1. Each model includes a

fifth order polynomial in driver tenure (in years) and is jointly significant at the 99% level.

28

Figures

Figure 1: Career Exit Probability by Driver Family connections

no family

father

son

brother